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Business Intelligence: Leveraging Power BI and Predictive Analytics

Elevating decision making and driving innovation with Power BI and predictive analytics

  • Schedule

    17 – 19 September 2024
    09.00 – 16.00 WIB

  • Jl. H. R. Rasuna Said No.Kav.20, Karet Kuningan, Setiabudi, Jakarta Selatan, DKI Jakarta 12940

  • Investment

    Rp. 5.550.000

18

Hours Course

WORKSHOP STARTS IN

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Overview

This course is meticulously designed to empower participants with advanced skills in business intelligence and data science. Spanning 15 comprehensive modules, the curriculum explores deep integrations between Power BI and Python, covering essential aspects such as data extraction, advanced visualization techniques, predictive analytics, and strategic use of regression models and machine learning. Participants will gain hands-on experience in leveraging the Power BI ecosystem, enhance their data transformation capabilities, and learn to employ Python for sophisticated data analytics tasks. The course uniquely focuses on integrating machine learning to elevate data analysis and visualization, enabling participants to apply these advanced techniques in real-world business scenarios, thereby boosting their capacity to drive informed decision-making and innovation in their organizations.

Course Syllabus

  • Overview of the Power BI Platform and Ecosystem: Desktop, Service, Mobile
  • Key Application Terminology and Visual Cues for Fields
  • Introduction to Power BI Querying Techniques
  • Understanding the Power BI Workflow for Effective Data Visualization
  • Key Elements of Visualization and Getting Started in Power BI
  • Exploring the Power BI Report View
  • Overview of Power BI File Types and Extensions
  • Steps for Creating and Managing a Live Data Connection with SQL
  • Techniques for Data Extraction and Transformation
  • Best Practices for Saving and Editing Data Sources in SQL
  • Methods for Modifying Data Types and Understanding Changes to Data
  • Techniques for Data Filtering and Creating Date Filters
  • Strategies for Data Sorting
  • Fundamentals of Data Modelling Including Relationships Between Tables and Cross-filtering
  • Techniques for Creating Crosstabs and Managing Totals and Subtotals
  • Best Practices for Creating Highlight Tables
  • How to solve business problems with data visualization
  • Understanding Data Visualization
  • How to extract insight from data visualization
  • How to create actionable insights
  • Benefits and Overview of Integrating Python with Power BI
  • Understanding the Role of Python Scripts in Enhancing Power BI Reports
  • Setting Up and Managing Anaconda Environment
  • Working with an IDE for Data Analysis
  • Introduction to Python Syntax and Key Programming Jargons
  • Fundamentals of Data Manipulation in Python: Dataframes, Data Types, and Exploratory Analysis
  • Techniques for Reading, Extracting, Subsetting, and Sampling Data
  • Foundations of Regression Analysis: Understanding and Applying OLS and Linear Models
  • Deep Dive into Multiple Regression: Techniques and Interpretations
  • Logistic Regression: From Theoretical Concepts to Practical Applications
  • Evaluating Model Performance: Techniques for Assessment and Selection
  • Techniques for Showing Correlations and Outliers with Scatter Plots
  • Understanding Data Distributions with Bins and Histograms
  • Core Principles and Layout Best Practices for Dashboard Design
  • Strategies for Selecting Appropriate Visuals for Various Data Types
  • Ensuring Accessibility and Inclusivity in Dashboard Design
  • Practical Steps for Embedding Python and R-based Models into Power BI
  • Use Cases for Predictive Analytics, Classification, and Clustering within Power BI
  • Tips for Seamless Integration of Machine Learning Models into Power BI Reports
  • Strategies for Visualizing Machine Learning Results in Power BI Dashboards
  • Techniques for Interpreting and Presenting Insights from Machine Learning Outputs
  • Best Practices for Report Deployment and Publishing
  • Techniques for Query Performance Optimization
  • Strategies for Ensuring Data Governance and Compliance

YOUR INSTRUCTOR

Handoyo Sjarif

Head of Business Intelligence at Algoritma Data Science School

As a Head of Business Intelligence and also a Senior Data Science Instructor at Algoritma Data Science School, Handoyo is deeply committed to making data science accessible, advocating for its capacity to enhance productivity and societal improvement. He integrates his proficiency in both Python and R programming languages into his instruction, enriching his teachings with a dual language perspective.

With an impressive repertoire of over 800 hours of teaching experience, Handoyo has been instrumental in providing consultative data science training to a range of prestigious clients, including PT Sigma Metrasys Solution, PT. Indosat Ooredoo, and Bank Central Asia. His involvement in significant initiatives, such as the Auto Insurance Fraud Analysis for Jasa Raharja, further underscores his hands-on expertise.

Course Receivables

  • Lecturer’s Notes

    Including Course Books (PDF), HTML files, course transcripts (if any).

  • Highly-accelerated Learning

    Learn under the assistance of mentorship of our lead instructor and a band of qualified teaching assistants throughout each course.

  • Certification of Completion

    Show current employer hat you’ve completed the course with a signed certificate of completion.

  • Quality Learning Environment

    We pay meticulous attention to the logistical details of our workshops: quality audio and visual setups, comfortable sitting arrangements, and small group size.

  • Refreshments & Coffee Break

    In our commitment to delivering a premium experience, we collaborate with leading catering services in Jakarta. Our aim is to ensure that all participants are served delectable lunches and revitalizing coffee breaks.

ABOUT THIS SERIES

Courses in this series cater to a diverse audience: from casual learners and working professionals to those venturing into data science and machine learning without a programming background.

We recognize that many students may not have prior expertise in statistics, mathematics, or algebra. Therefore, our courses are designed with a gentle learning curve, placing a strong emphasis on hands-on experience and individualized instruction. Our dedicated team of instructors and teaching assistants ensure personalized guidance every step of the way.

Teaching Methodology:

Students work through tons of real-life examples using sample datasets donated by our mentors and corporate partners. We believe in a learn-by-building approach, and we employ instructors who are uncompromisingly passionate about your growth and education.